Kazuma Iteya
Papers
1
Total Citations
5
H-Index
1
About
Kazuma Iteya’s research focuses on advancing robot navigation and computer vision, particularly through innovative methods for distance estimation from single images. His key contributions address the fundamental challenge of inferring spatial depth without stereo or depth sensors, a critical problem for autonomous systems. In his most cited work, “Distance estimation with 2.5D anchors and its application to robot navigation” (2018), Iteya introduced a novel concept of 2.5D anchors—candidate distances that help regress object depth from a single image despite wide variations in appearance. This approach significantly improves the robustness and accuracy of distance estimation, enabling more reliable robot navigation in unstructured environments. While his citation count (5) reflects early-stage impact, the work lays important groundwork for monocular depth estimation techniques. Iteya’s research bridges computer vision and robotics, offering practical solutions for real-world deployment of autonomous systems. His contributions are particularly valuable for students and researchers exploring cost-effective navigation methods that reduce reliance on expensive sensor suites.
Research Focus
Key Achievements
Top Papers
- 1